Phylogenetic Monte Carlo: Quantifying Uncertainty in Phylogenetic Methods (now on CRAN)
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Latest commit 87f61b9 Jun 11, 2016 @cboettig format DOI

README.md

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Beta, use with caution!

This is a lightweight implementation of my pmc package focusing on what I think are the more common use cases (e.g. it will no longer support comparisons of a geiger model against an ouch model). Further, it does not cover many of the newer model fitting that have been implemented since pmc was first released.

The goal of this release is mostly to provide compatibility with current versions of geiger.

Getting started

Install the package:

library("devtools")
install_github("cboettig/pmc2")

A trivial example with data simulated from the lambda model.

library("pmc")
library("geiger")
## Loading required package: ape
phy <- sim.bdtree(n=10)
dat <- sim.char(rescale(phy, "lambda", .5), 1)[,1,]
out <- pmc(phy, dat, "BM", "lambda", nboot = 50)

Plot the results:

dists <- data.frame(null = out$null, test = out$test)
library("ggplot2")
library("tidyr")
library("dplyr")
## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
dists %>% 
  gather(dist, value) %>%
  ggplot(aes(value, fill = dist)) + 
  geom_density(alpha = 0.5) + 
  geom_vline(xintercept = out$lr)

Citation

Carl Boettiger, Graham Coop, Peter Ralph (2012) Is your phylogeny informative? Measuring the power of comparative methods, Evolution 66 (7) 2240-51. http://doi.org/10.1111/j.1558-5646.2011.01574.x